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ADK deployment specialist covering Cloud Run, GKE, Vertex AI Agent Engine, and Docker containerization. Use when deploying ADK agents to production, configuring cloud infrastructure, or setting up CI/CD pipelines for ADK applications.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
ADK Agent-to-Agent (A2A) protocol expert covering remote agent communication, agent cards, HTTP-based delegation, and distributed agent systems. Use when building multi-service agent architectures, implementing remote agent calls, or designing agent networks.
ADK memory + embeddings across databases. Generate multimodal embeddings with gemini-embedding-2, persist agent sessions/memory to Cloud SQL, Postgres, Firestore, or SQLite, and store/retrieve vectors for RAG (pgvector, Firestore vector). Use when choosing a session/memory backend, wiring a database SessionService or MemoryService, generating embeddings, or building a vector store for retrieval on an ADK agent.
Migrate ADK agents from 1.x to 2.3. Covers breaking API changes (graph Workflow vs GraphAgent, McpToolset, BuiltInCodeExecutor, ctx.resume_inputs, agent modes), renamed imports, deploy CLI changes, model/thinking/embedding updates, and session/state incompatibility. Use when upgrading an ADK 1.x codebase to 2.3, auditing code for deprecated 1.x APIs, or resolving 2.3 import/attribute errors.
ADK model routing, thinking/effort levels, and fallback methodology for production backends. Covers the current Gemini 3.x model catalog (gemini-3.5-flash, gemini-3.1-flash-lite, gemini-3.1-pro-preview, gemini-3.1-flash-live-preview, gemini-embedding-2), thinking_level effort tiers (minimal/low/medium/high), deprecation of thinking_budget and sampling params, and primary→fallback chains. Use when selecting a model, tuning thinking/effort, routing by task complexity/cost, or designing model fallback for an ADK agent.
Central hub for building, testing, and iterating on ADK agents. Trigger this skill when the user wants to create a new agent, configure modes (task, single-turn), or build graph-based workflows.
DAG-based ADK workflow in Go using graph pattern. Use for Go backends needing multi-step agent pipelines.
SOC 職業分類に基づく
| name | adk-deployment |
| description | ADK deployment specialist covering Cloud Run, GKE, Vertex AI Agent Engine, and Docker containerization. Use when deploying ADK agents to production, configuring cloud infrastructure, or setting up CI/CD pipelines for ADK applications. |
You are a senior DevOps engineer specializing in deploying Google ADK applications to production environments.
references/ folder:
references/cloud-run.md - Cloud Run deploymentreferences/gke.md - Google Kubernetes Enginereferences/agent-engine.md - Vertex AI Agent Enginereferences/index.md - Deployment overview| Platform | Use Case | Command |
|---|---|---|
| Local Dev | Development | adk web or uvicorn main:app --reload |
| Cloud Run | Serverless | adk deploy cloud_run |
| GKE | Kubernetes | kubectl apply -f deployment.yaml |
| Vertex AI | Managed | adk deploy agent_engine |
.env files for secrets (GOOGLE_API_KEY, etc.)Dockerfile for containerizationdeployment.yaml / deployment_rbac.yaml for Kubernetescloudbuild.yaml for CI/CD